Faculty Bibliography

Although there are changes in gene expression and alterations in neuronal density and afferent inputs in the forebrain of trisomic mouse models of Down syndrome (DS) and Alzheimer's disease (AD), there is a lack of systematic assessments of gene expression and encoded proteins within individual vulnerable cell populations, precluding translational investigations at the molecular and cellular level. Further, no effective treatment exists to combat intellectual disability and basal forebrain cholinergic neurodegeneration seen in DS. To further our understanding of gene expression changes before and following cholinergic degeneration in a well-established mouse model of DS/AD, the Ts65Dn mouse, we assessed RNA expression levels from CA1 pyramidal neurons at two adult ages (∼6 months of age and ∼11 months of age) in both Ts65Dn and their normal disomic (2N) littermates. We further examined a viable therapeutic, maternal choline supplementation (MCS), which has been previously shown to lessen dysfunction in spatial cognition and attention, and have protective effects on the survival of basal forebrain cholinergic neurons (BFCNs) in the Ts65Dn mouse model. Results indicate that MCS normalized expression of several genes in key gene ontology categories, including synaptic plasticity, calcium signaling, and AD-associated neurodegeneration related to amyloid-beta peptide (Aβ) clearance. Specifically, normalized expression levels were found for endothelin converting enzyme-2 (Ece2), insulin degrading enzyme (Ide), Dyrk1a, and calcium/calmodulin-dependent protein kinase II (Camk2a), among other relevant genes. Single population expression profiling of vulnerable CA1 pyramidal neurons indicates that MCS is a viable therapeutic for long-term reprogramming of key transcripts involved in neuronal signaling that are dysregulated in the trisomic mouse brain which have translational potential for DS and AD.

BACKGROUND: Deficits in N-methyl-d-aspartate-type (NMDAR) function contribute to symptoms and cognitive dysfunction in schizophrenia. The efficacy of NMDAR agonists in the treatment of persistent symptoms of schizophrenia has been variable, potentially reflecting limitations in functional target engagement. We recently demonstrated significant improvement in auditory mismatch negativity (MMN) with once-weekly treatment with d-serine, a naturally occurring NMDAR glycine-site agonist. This study investigates effects of continuous (daily) NMDAR agonists in schizophrenia/schizoaffective disorder. METHODS: Primary analysis was on MMN after double-blind crossover (60mg/kg/d, n=16, 6weeks) treatment with d-serine/placebo. Secondary measures included clinical symptoms, neurocognition, and the effects of open-label (30-120mg/kg/d, n=21) d-serine and bitopertin/placebo (10mg, n=29), a glycine transport inhibitor. RESULTS: Double-blind d-serine treatment led to significant improvement in MMN frequency (p=0.001, d=2.3) generation and clinical symptoms (p=0.023, d=0.80). MMN frequency correlated significantly with change in symptoms (r=-0.63, p=0.002) following co-variation for treatment type. d-Serine treatment led to a significant, large effect size increase vs. placebo in evoked alpha-power in response to standards (p=0.036, d=0.81), appearing to normalize evoked alpha power relative to previous findings with controls. While similar results were seen with open-label d-serine, no significant effects of bitopertin were observed for symptoms or MMN. CONCLUSIONS: These findings represent the first randomized double-blind placebo-controlled study with 60mg/kg d-serine in schizophrenia, and are consistent with meta-analyses showing significant effects of d-serine in schizophrenia. Results overall support suggest that MMN may have negative, as well as positive, predictive value in predicting efficacy of novel compounds. CLINICAL TRIALS REGISTRATION: Clinicaltrials.gov: NCT00322023/NCT00817336 (d-serine); NCT01116830 (bitopertin).

In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an "effect modifier". Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration.

Antidepressant medications are commonly used to treat depression, but only about 30% of patients reach remission with any single first-step antidepressant. If the first-step treatment fails, response and remission rates at subsequent steps are even more limited. The literature on biomarkers for treatment response is largely based on secondary analyses of studies designed to answer primary questions of efficacy, rather than on a planned systematic evaluation of biomarkers for treatment decision. The lack of evidence-based knowledge to guide treatment decisions for patients with depression has lead to the recognition that specially designed studies with the primary objective being to discover biosignatures for optimizing treatment decisions are necessary. Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) is one such discovery study. Stage 1 of EMBARC is a randomized placebo controlled clinical trial of 8 week duration. A wide array of patient characteristics is collected at baseline, including assessments of brain structure, function and connectivity along with electrophysiological, biological, behavioral and clinical features. This paper reports on the data analytic strategy for discovering biosignatures for treatment response based on Stage 1 of EMBARC.

Latent class models are widely used to identify unobserved subgroups (i.e., latent classes) based upon one or more manifest variables. The probability of belonging to each subgroup is typically modeled as a function of a set of measured covariates. In this paper, we extend existing latent class models to incorporate matrix covariates. This research is motivated by a randomized placebo-controlled depression clinical trial. One study goal is to identify a subgroup of subjects who experience symptoms improvement early on during antidepressant treatment, which is considered to be an indication of a placebo rather than a true pharmacological response. We want to relate the likelihood of belonging to this subgroup of early responders to baseline electroencephalography (EEG) measurement that takes the form of a matrix. The proposed method is built upon a low rank Candecomp/Parafac (CP) decomposition of the target coefficient matrix through low-dimensional latent variables, which effectively reduces the model dimensionality. We adopt a Bayesian hierarchical modeling approach to estimate the latent variables, which allows a flexible way to incorporate prior knowledge about covariate effect heterogeneity and offers a data-driven method of regularization. Simulation studies suggest that the proposed method is robust against potentially misspecified rank in the CP decomposition. With the motivating example we show how the proposed method can be applied to extract valuable information from baseline EEG measurements that explains the likelihood of belonging to the early responder subgroup, helping to identify placebo responders and suggesting new targets for the study of placebo response.

PURPOSE/BACKGROUND: Deficits in N-methyl-D-aspartate receptor (NMDAR) function contribute to symptoms and cognitive dysfunction in schizophrenia and are associated with impaired generation of event-related potential measures including auditory mismatch negativity. Parallel studies of the NMDAR agonist d-serine have suggested that sensitivity of these measures to glutamate-based interventions is related to symptomatic and cognitive response. Bitopertin is a selective inhibitor of glycine transport. This study investigates effects of bitopertin on NMDAR-related event-related potential deficits in schizophrenia. METHODS/PROCEDURES: Patients with schizophrenia/schizoaffective disorder were treated with bitopertin (10 mg, n = 29), in a double-blind, parallel group investigation. Auditory mismatch negativity served as primary outcome measures. Secondary measures included clinical symptoms and neurocognitive performance. FINDINGS/RESULTS: No significant changes were seen with bitopertin for neurophysiological, clinical, or neurocognitive assessments. IMPLICATIONS/CONCLUSIONS: These findings represent the first assessment of the effect of bitopertin on neurophysiological biomarkers. Bitopertin did not significantly affect either symptoms or NMDAR-related biomarkers at the dose tested (10 mg). Mismatch negativity showed high test-retest reliability, supporting its use as a target engagement measure.

BACKGROUND: Moderators of differential psychotherapy outcome for posttraumatic stress disorder (PTSD) are rare, yet have crucial clinical importance. We tested the moderating effects of trauma type for three psychotherapies in 110 unmedicated patients with chronic DSM-IV PTSD. METHODS: Patients were randomized to 14 weeks of prolonged exposure (PE, N = 38), interpersonal psychotherapy (IPT, N = 40), or relaxation therapy (RT, N = 32). The Clinician-Administered PTSD Scale (CAPS) was the primary outcome measure. Moderator candidates were trauma type: interpersonal, sexual, physical. We fit a regression model for week 14 CAPS as a function of treatment (a three-level factor), an indicator of trauma type presence/absence, and their interactions, controlling for baseline CAPS, and evaluated potential confounds. RESULTS: Thirty-nine (35%) patients reported sexual, 68 (62%) physical, and 102 (93%) interpersonal trauma. Baseline CAPS scores did not differ by presence/absence of trauma types. Sexual trauma as PTSD criterion A significantly moderated treatment effect: whereas all therapies had similar efficacy among nonsexually-traumatized patients, IPT had greater efficacy among sexually traumatized patients (efficacy difference with and without sexual trauma: IPT vs. PE and IPT vs. RT P's < .05), specifically in PTSD symptom clusters B and D (P's < .05). CONCLUSIONS: Few studies have assessed effects of varying trauma types on effects of differing psychotherapies. In this exploratory study, sexual trauma moderated PTSD outcomes of three therapies: IPT showed greater benefit for sexually traumatized patients than PE or RT. The IPT focuses on affect to help patients determine trust in their current environments may particularly benefit patients who have suffered sexual assault.

Perceived social support (PSS) has been related to physical and mental well-being in typically developing individuals, but systematic characterizations of PSS in autism spectrum disorder (ASD) are limited. We compared self-report ratings of the multidimensional scale of PSS (MSPSS) among age- and IQ-matched groups of adults (18-58 years) with cognitively high-functioning ASD (N = 41), or attention-deficit/hyperactivity disorder (ADHD; N = 69), and neurotypical controls (NC; N = 69). Accompanying group comparisons, we used machine learning random forest (RF) analyses to explore predictors among a range of psychopathological and socio-emotional variables. Relative to both ADHD and NC, adults with ASD showed lower MSPSS ratings, specifically for the friends subscale (MSPSS-f). Across ASD and ADHD, interindividual differences in autism severity, affective empathy, symptoms of anxiety related to social interactions, hyperactivity/impulsivity, and somatization best predicted MSPSS-f. These relationships did not differ between clinical groups. While group comparisons demonstrated greater impairment in individuals with ASD, analyzing individuals' characteristics revealed cross-diagnoses similarities in regard to their MSPSS-f relationships. This is consistent with the Research Domain Criteria framework, supporting a trans-diagnostic approach as on the path toward "precision medicine." Autism Res 2017. (c) 2017 International Society for Autism Research, Wiley Periodicals, Inc.

UNLABELLED:Remission rates for Major Depressive Disorder (MDD) are low and unpredictable for any given antidepressant. No biological or clinical marker has demonstrated sufficient ability to match individuals to efficacious treatment. Biosignatures developed from the systematic exploration of multiple biological markers, which optimize treatment selection for individuals (moderators) and provide early indication of ultimate treatment response (mediators) are needed. The rationale and design of a multi-site, placebo-controlled randomized clinical trial of sertraline examining moderators and mediators of treatment response is described. The target sample is 300 participants with early onset (≤30 years) recurrent MDD. Non-responders to an 8-week trial are switched double blind to either bupropion (for sertraline non-responders) or sertraline (for placebo non-responders) for an additional 8 weeks. Clinical moderators include anxious depression, early trauma, gender, melancholic and atypical depression, anger attacks, Axis II disorder, hypersomnia/fatigue, and chronicity of depression. Biological moderator and mediators include cerebral cortical thickness, task-based fMRI (reward and emotion conflict), resting connectivity, diffusion tensor imaging (DTI), arterial spin labeling (ASL), electroencephalograpy (EEG), cortical evoked potentials, and behavioral/cognitive tasks evaluated at baseline and week 1, except DTI, assessed only at baseline. The study is designed to standardize assessment of biomarkers across multiple sites as well as institute replicable quality control methods, and to use advanced data analytic methods to integrate these markers. A Differential Depression Treatment Response Index (DTRI) will be developed. The data, including biological samples (DNA, RNA, and plasma collected before and during treatment), will become available in a public scientific repository. CLINICAL TRIAL REGISTRATION:Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC). Identifier: NCT01407094. URL: http://clinicaltrials.gov/show/NCT01407094.

Understanding heterogeneity in phenotypical characteristics, symptoms manifestations and response to treatment of subjects with psychiatric illnesses is a continuing challenge in mental health research. A long-standing goal of medical studies is to identify groups of subjects characterized with a particular trait or quality and to distinguish them from other subjects in a clinically relevant way. This paper develops and illustrates a novel approach to this problem based on a method of optimal-partitioning (clustering) of functional data. The proposed method allows for the simultaneous clustering of different populations (e.g., symptoms of drug and placebo treated patients) in order to identify prototypical outcome profiles that are distinct from one or the other treatment and outcome profiles common to the different treatments. The clustering results are used to discover potential treatment effect modifiers (i.e., moderators), in particular, moderators of specific drug effects and placebo response. A depression clinical trial is used to illustrate the method.